Bisphenol A increases aP2 expression in 3T3L1 by enhancing the transcriptional activity of nuclear receptors at the promoter
Bibliographic record
Abstract
Environmental pollutants, such as bisphenol A (BPA), have the potential to affect the differentiation processes and the biology of the adipose tissue. The 3T3-L1 model is one of the murine cell models used extensively for the investigation of the molecular events that govern the differentiation of adipocytes from a committed preadipocyte to a mature, lipid laden adipocyte. Most of the studies investigating the effects of BPA on preadipocyte differentiation have investigated the effects of this chemical in the presence of an optimal differentiation cocktail containing high concentrations of the synthetic glucocorticoid dexamethasone, conditions that result in 90% to 100% of differentiated adipocytes. Our studies employed the 3T3-L1 cell model in the absence of exogenous glucocorticoids. We show that BPA is able to increase the differentiation of the 3T3-L1 cells under these conditions. Furthermore, the effect of BPA was observed in the absence of the synthetic glucocorticoid (dexamethasone), a hormone known to be required for the differentiation of the 3T3-L1 cells. In addition, BPA upregulated the mRNA expression and protein levels of the terminal marker of adipogenesis the fatty acid binding protein (aP2) in these cells. Interestingly, the known modulators of adipogenesis such as the peroxisome proliferator-activated receptor (PPAR) γ or CCAAT enhancer binding protein (C/EBP) α were not elevated at the mRNA or protein level in response to BPA. Furthermore, BPA upregulated the expression levels of the marker of adipogenesis aP2, through an effect on the transcriptional activity of C/EBPδ and the glucocorticoid receptor (GR) at its promoter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".